Python

These days, I highly prefer coding in Python as compared to other languages that I previously used like Matlab or R. However, I have always wondered when data science teams should use one programming language over another for certain tasks. If all team members know R and Python equally well and need to train a machine learning model, which language should they use? How could they use both Python and R without redundancies? We will discuss how to best leverage both R and Python for building data science workflows. Firstly, it really helps to know the strengths and weaknesses of Python and R. Python has overtaken R in popularity for machine learning, but R is pretty awesome at visualizing data as plots and/or dashboards. Deciding whether to exclusively use Python or R on a data science project is a big hairy topic and the answer depends on a number of factors, but this article provides lots of guidance to help data scientists make an informed decision. ...

Ever want to generate a new repo based on a predefined template? Now you can using Cookiecutter! I will show you how to easily spin up a fresh Cookiecutter repo for your latest data science project in Python. Cookiecutter is an awesome command-line tool and Python package that creates projects (aka populates repo folders) based on cookiecutters (or project templates). What does this mean? Goodbye to manually copying and pasting old project repos. Now, you can automatically clone one of the thousands of cookiecutters or you can create your own. ...

This article will explore possible items to include your Git repo README file. We will discuss several items that we can include in a README and when to include them. What is a README? A README file is a text file that explains a project to new user. It helps users quickly understand where to find information on the project such as brief description of your project, how to install/fork it and/or how to use your code. READMEs live in the top level directory of your repo in GitHub, Bitbucket or GitLab. ...

We know that you’ve probably heard of object oriented programming (OOP), but outside of designing games, when is best to use it for data science? We haven’t used OOP much until recently when we refactored a data science project code base. In this article, we give a brief refresher for OOP and discuss our top reasons for using OOP with a few examples. When we code, we generally skew towards more of a functional style of programming, but sometimes we get frustrated with code that is too long. This means that we focus on building our code using functional modules of code and functions. However, at some point, our functions become too large to keep track of and we consider refactoring into classes. ...

Today, I wondered whether I could automatically save an image of colored text from the Python console. I was looking for a way to display very long strings that automatically wrapped to its container, so I avoided the dreaded run-on string that never ends. Also, could I save the image elegantly with high resolution? In this article, I will discuss potential approaches for getting a file with nice-looking colored text that can be programmatically generated. ...

On Aug 21st, 2020, fastai released a new version of their Practical Deep Learning for Coders -Part 1 course. This course is a must-take for new and intermediate deep learning practitioners. It is well done and teaches you intuition without drowning you in theory. The only prerequisites are some high-school math, and a year of coding experience (preferably in Python). This course is free and can be done without any installation, by taking advantage of the Colab and/or Gradient platforms, which provide free, GPU-powered Python notebooks. Go here to learn more about the course. ...